Finding the best approach for the transportation sector to synergistically reduce CO2 and air pollutants emissions

Finding the best approach for the transportation sector to synergistically reduce CO2 and air pollutants emissions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Finding the best approach for the transportation sector to synergistically reduce CO2 and air pollutants emissions Yuan Wang, Liying Ping, Lien-Chieh Lee, Hongyu Zhang, Binbin Peng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1508775/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The transportation sector has a significant impact on the synergistic reduction of CO 2 and pollutants. Using a structural path decomposition, we reveals the direct and embodied CO 2 and NOx emissions from the transportation sector triggered by key supply chain pathways. Meanwhile, the emission abatement potential and economic costs of 33 vehicle specific abatement technology options are analyzed based on the input-output analysis for life cycle assessment. The results show that most of the vehicle technology options can reduce CO 2 and NOx emissions while saving economic costs. Among these technologies, both the pure electric technology for passenger cars and the parallel hybrid technology for heavy-duty trucks have high abatement potential. Compared to costly pure electrification technologies for passenger cars, parallel hybrid technologies for heavy-duty trucks offer the highest economic benefits as well as a better driving mileage. Therefore, hybrid heavy-duty trucks will be a more comprehensive solution in the near future. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The transportation sector plays an important role in the economic activities of society by linking production, exchange, distribution and consumption. Meanwhile, the transportation sector is a major source of CO 2 and air pollutants emissions [ 1 ][ 2 ] , with road subsector making the largest contribution [ 3 ] . The China's latest national plan (14th Five-Year Plan) puts forward the requirement of synergy in carbon and air pollutants reduction, and the transportation sector has a significant impact on achieving this goal. China has now become very stringent in managing transportation emissions, especially in vehicles. A range of direct and enforceable end-of-pipe treatment policies, such as improved fuel quality and stricter tailpipe emission standards, have achieved significant emission reductions [ 4 ][ 5 ] . However, vehicle emissions remain high as transportation demand increases. Transportation is the demand generated by economic activities. The analysis of emissions from the transportation sector inevitably involves all sectors of the economy. Therefore, it is important to explore the economic relationship between the transportation sector and other sectors, which requires extremely comprehensive data. The input-output table is the most appropriate tool because it is able to link the production of goods to the exchange of materials between economic sectors. The input-output analysis have shown that heavy industry, electricity, construction and services are the main sectors related to air pollutants and CO 2 emissions from the transport sector [ 6 ][ 7 ] . The final demand is the strongest driver of the rapid growth of CO 2 emissions from the transportation sector [ 8 ] . While these studies have contributed significantly to the understanding of transportation emissions and have generated useful production and demand-side policy recommendations. However, facing the prominent position of transportation emission, it is not enough to rely on macro level analysis alone. Research on specific abatement technologies for the transportation sector, especially the road subsector, is essential. Several studies have assessed the abatement potential and costs of vehicle emission abatement technologies at the micro-level. For example, International Council on Clean Transportation (ICCT) studied the abatement potential and cost of vehicles over the time period 2015 to 2030 [ 9 ][ 10 ] . It found that the investment cost of vehicle abatement technology rises as abatement rates increase, but consumer savings in fuel costs will be two to three times the cost of vehicle technology investments by 2030. Peng et al. [ 11 ] studied 55 passenger car abatement technologies in China from 2010 to 2030, with abatement costs ranging from − 1324.59 to 7623.47 yuan/t from 2010 to 2030, with a cumulative CO 2 abatement potential of about 27Mt. However, these studies mainly analyzed the potential and costs of energy-efficient technologies in the use phase, ignoring the emissions of these technologies in the production phase.. New energy technologies have high CO 2 emissions in the production phase, which can reach up to 50% of the emissions reductions in the use phase [ 12 ] . In the coming decades, production emissions will account for an increasing share of new energy vehicle emissions. Meanwhile, the response of global temperature to the impulse of emitted greenhouse gases is an important factor in taking productive emissions into account. The temperature impulse response of global temperature has a lag and will peak in a few decades. Production emissions now will affect temperatures decades into the future [ 13 ] . Inadequately, the above study only measured the emissions of new energy vehicles, such as hybrid and electric vehicles, at the production stage, without fully analyzing the emissions of the whole transportation industry and comparing more abatement technologies at the production stage. Based on the above considerations, this paper integrates life cycle assessment based on input-output analysis (IOA-LCA) and structural path analysis (SPA) to trace the CO 2 and air pollutants emission streams hidden in the supply chain of the whole transportation sector and 33 combination scenarios of vehicle types and technologies from a life-cycle perspective, including production phase and use phase. This innovation is that the additional emissions generated by all vehicle abatement technologies at the equipment manufacturing and energy production stages are quantitatively assessed, and the net abatement effect of each technology at the whole economic system level is evaluated. Combined with the net abatement potential and economy cost-benefit analysis, the future motor vehicle abatement technologies for the synergistic reduction of air pollutants and CO 2 in China are proposed in this paper. Results Change and driving force analysis of CO 2 and NOx emissions in the transportation sector According to the CO 2 and NOx emissions of 11 subsectors of transportation over the period 2012–2018, we find CO 2 emissions from the transportation sector increased substantially while NOx emissions remained essentially unchanged, and the road freight contributes the most to the increase of CO 2 emissions, accounting for about 40%, followed by the multimodal transport subsector and the road passenger transport subsector (Fig. 1 ). Furthermore, we apply the structural decomposition analysis (SDA) method to analyze the effects of emission intensity, industrial structure, and final demand on the drivers of changes in CO 2 and NOx emissions. The overall SDA calculation method can be seen in Supporting Information section 1.2. The factors causing the rapid increase in CO 2 emissions are the simultaneous increase in both emission intensity and final demand. The reduction of NOx emission intensity offsets the increase of final demand, and the impact of industrial structure on CO 2 and NOx is not significant. The reasons for the above phenomenon are related to environmental protection policies. From 2013 to 2018, China introduced a series of mobile source emission reduction measures to reduce NOx emissions, which achieved significant emission reduction effects [ 4 ][ 5 ] . However, China has not issued standards or policies for CO 2 emission reduction of motor vehicles, resulting in high and rapid growth of CO 2 emissions in the transportation sector. Emission flows between consumption and production Emission flows of transportation sector To explore more systematically the emissions of transportation sector in its production stage, we use the modified SPA method to provide an exhaustive diagram of the supply chain linkages related to the transport final consumption attribution and final production attribution, highlighting the emissions implicit in the intermediate product streams that connect different economic sectors along the supply chain. From the perspective of final consumption attribution, the emissions include the direct emissions and embodied emission flows [ 14 ] . According to Fig. 2 , the share of direct emissions from transportation is very high. The direct emissions of CO 2 and NOx account for 50% and 81% of all emissions, respectively. Regarding the embodied CO 2 emission flows, the electricity is significant in each production layer. For other sectors, the story is quite different. Refined oil and auto parts sectors are significant in production layer PL1 (accounting for 28% in the total embodied emissions flows), while the mining, metal products, and equipment manufacturing sectors are significant in PL2 and PL3 (accounting for 35% respectively). For NOx, both the direct emissions and the embodied emissions from transportation are dominant in all production layers, accounting for 65%, 40%, and 34% of the total embodied emissions in PL1, PL2, and PL3, respectively. From the aspect of final production, the CO 2 and NOx emissions due to transportation during production are very high (leftmost column of Fig. 2 ). This is similar to the findings revealed in the road freight study by Liu et al. [ 6 ] . In summary, CO 2 emissions from China's transportation sector do not decrease but increase, while the NOx emissions only decrease slightly. Therefore, it is not enough to focus only on the industry sector scale, and a more detailed analysis of the whole life cycle of the technical path is needed. Emission flows of typical technologies In order to analyze the additional emissions generated by motor vehicle emission reduction technologies, we calculate the emission flows caused by the consumption of intermediates in the production process of 33 combination scenarios of vehicle types and technologies, and the comparative analysis of each technology is presented in section 3.3.1 below. We take the example of the purely electric technology for passenger cars scenario (PC12), which had the highest production costs and the most complex production process. By using the SPA method, we quantitatively analyze the emission flows caused by this technology, including additional emissions from electric vehicle manufacturing and power production, as well as additionally reduced emissions from fuel production (Fig. 3 ). Different from the results of analyzing the whole transportation sector with SPA method, the auto parts and steel rolling sector has also become a prominent contributor to emission flows in the SPA results of PC12 scenario alone, which is responsible for approximately 50% of all CO 2 embodied emissions and 40% of all NOx embodied emissions in the PL1, PL2 and PL3 (Fig. 3 (a)~(b)). The production of electricity and fuel oil is different from the complex motor vehicle manufacturing industry, with simple intermediate production links (Fig. 3 (c)~(f)). The result shows that if all existing passenger cars are converted to electric vehicles, although it will save emissions by 470.44 Mt CO 2 and 648.28 Kt NOx from fuel oil production, it will cause an additional emission about 494.44 Mt CO 2 and 676.13 Kt NOx in the electric vehicles manufacturing phase. These two parts almost completely offset each other. Moreover, the electric vehicles require about 216.33 Mt CO 2 and 80.39 Kt NOx emissions from additional electric power production. Therefore, the low carbon of power production is the key to the emission reduction of electric vehicle technology. At the same time, it should also be taken into account that due to fuel oil conservation, emissions in the production stage are reduced, offsetting the additional emissions generated by the manufacture of electric vehicles. Analysis of emission abatement potential and costs of typical vehicle energy-saving technologies In order to analyze the net actual impact of the implementation of typical energy-saving technologies for vehicles on pollutant emissions, we comprehensively consider the emissions in the use stage and production stage, and analyze the engine energy-saving technology, new energy technology and drag reduction technology of passenger cars, light trucks and heavy trucks. Analysis of emission abatement potential over the whole life cycle (1) Emission reductions in the production and use stage of each technology Based on the above, the SPA method is applied to calculate the emissions in the production stage of all 33 combination scenarios of vehicle types and technologies. The results show that the additional emissions resulting from the production phase of all technologies are smaller than emission reductions from their use phase, indicating that all 33 combination scenarios of vehicle types and technologies could achieve net emission reductions (Fig. 4 ). In terms of vehicle types, passenger cars and heavy trucks have greater potential of CO 2 and NOx emission reductions. Regarding technologies, new energy technology (T-Ⅱ) has the highest emission reduction potential. It is worth noting that the hybrid and pure electrification technologies have higher CO 2 emissions in the production phase than others. Especially, for passenger car plug-in hybrid (PC11) and passenger car pure electric (PC12) technologies, the CO 2 emission of vehicle production could reach 50% of the emission reduction in the use stage. Unlike CO 2 , it could be found that the additional NOx emissions caused by each type of technology in their production phase are small, and the emission reduction in the use phase plays a dominant role in the net emission abatement (Fig. 4 (b)). Although there are differences in the main emission stages, these technologies with high CO 2 reduction potential would also have high reduction potential for NOx. Especially, the new energy technologies have the best synergistic effect on reducing both CO 2 and NOx. (Fig. 4 (c) and Table S4). Net benefit analysis Figure 4 (d) shows the net benefits ( \(NB\) ) of the 33 vehicle types and technology combination options. It could be found that most technologies are cost-effective. The economic benefits of fuel saving in the use stage are greater than the additional invest costs of energy-saving technologies, except for passenger car plug-in hybrid technology (PC11) and passenger car pure electric technology (PC12). The PC11 and PC12 technology have negative net benefits because their additional investment costs and electricity costs exceed the fuel savings benefits. According to Table 1 , the drag reduction technologies category has the highest net abatement benefit of CO 2 on average. For NOx, however, the engine energy-saving technology category has the highest net abatement benefit on average. New energy technology has the lowest net benefit per unit reduction of CO 2 and NOx (see Table 1 ). For instance, from the whole life cycle perspective, the costs of PC12 technology are the highest and the net abatement benefits per unit of CO 2 and NO X are − 1424 yuan/t and − 75 yuan/kg, respectively. However, according to the above analysis of abatement potential, compared with other technologies, the PC12 technology scenario has the greatest emission reduction potential. Therefore, the promotion of this technology requires additional long term massive subsidies. However, according to the latest national energy policy, China will no longer give subsidies for the purchase of new electric vehicles on December 31, 2023 [ 15 ] . The withdrawal of fuel vehicles may be a mandatory policy in the future. Therefore, the costs would be transferred to consumers. We notice that heavy truck parallel hybrid technology scenario (HT3) also has a large emission reduction potential, with the highest CO 2 reduction potential and the second highest NOx reduction potential after PC12 (Fig. 4 (a) - (b)), but the HT3 technology scenario has the highest net benefit among all technologies (Fig. 4 (d)). Therefore, this technology is cost-effective and worth popularizing. The net abatement benefits per unit emissions ( \(AB\) ) for each technology are shown in Supplementary Information Section 3 and Table S5. Table 1 Average net abatement benefit per unit for each type of technology Average net abatement benefit per unit Engine energy-saving technology New energy technology Drag reduction technology PC LT HT Average PC LT HT Average PC LT HT Average CO 2 (yuan/t) 1,460 1,865 1,863 1,676 264 1,278 1,750 703 2,165 2,056 1,789 2,019 NOx (yuan/kg) 101 1,386 350 648 26 941 328 293 163 1,533 337 626 Note: PC is passenger car, LT is light truck, HT is heavy truck. Discussion The current emission reduction measures of transportation are weak for CO 2 control. CO 2 emissions from the transportation sector have increased rapidly, from 317.6 Mt in 2012 to 746.2 Mt in 2018. In future, the transportation sector will be critical for the entire economic system to achieve the goal of reducing pollutants and carbon. The transportation policies of China should focus more on the coordinated emission reduction of CO 2 and air pollutants. To examine the relationship between the transportation sector and other sectors, we use the SPA method to make a detailed profile of production emissions due to the transportation sector, including direct and embodied emissions. The transportation sector causes much higher direct emissions than embodied emissions. Direct emissions of CO 2 and NOx from the production phase account for 50% and 81% of all emissions, respectively. This is because the direct emissions in most production phase are related to road freight [ 6 ] . The transportation industry itself will form a vicious circle, that is, the increase of the scale of the transportation industry will drive the development of electricity, refined petroleum, auto parts, mining, equipment manufacturing, and metal products industries, and these industries need more transportation support for their production, resulting in more emissions. Therefore, it is suggested to formulate low-carbon policies and plans for vehicles and their power production stages. Special attention should be paid to the freight emissions caused by the production stage. From a more micro technical point of view, 33 vehicle abatement technology scenarios are analyzed in this paper. Most of them have good synergistic CO 2 and NOx reduction effects. In addition, we find that all technology scenarios, except for pure electric (PC12) and plug-in hybrid (PC11) technologies for passenger cars, have net benefits due to fossil fuel savings. Notably, the passenger car pure electric technology (PC12) has the highest potential for emission reduction, but also requires the highest cost. Another noteworthy point is that the parallel hybrid technology for heavy trucks (HT3) has the greatest potential to reduce emissions, which is consistent with the findings of Zhao et al. [ 16 ] and Xu et al. [ 17 ] . Moreover, this technology has the largest net benefit. In terms of technical principle, the parallel hybrid technology of heavy truck is modified based on the traditional fuel vehicle, and its key is dual engine and electronic auxiliary unit [ 18 ] . Therefore, the modification cost is low. The pure electric trucks cannot meet the long-distance transportation requirements, while the pollution emission of traditional diesel power trucks is too high. The parallel hybrid technology for trucks can make up for the shortcomings of diesel power and pure electric technology, and meet the needs of transportation and emission reduction at the same time. Currently, heavy trucks using parallel hybrid technology are more common in Europe [ 19 ] , while they are rare in China. The reason is that in China hybrid heavy trucks do not have the same financial subsidies as pure electric passenger cars, resulting in low enthusiasm for hybrid heavy trucks production. Combined with the above research conclusions on the important role of freight transportation in the production stage, it is suggested to formulate policies to promote parallel hybrid technology for heavy trucks. Under the background of China's national strategy of green freight and optimization of traffic structure, the promotion of hybrid heavy trucks may be a more win-win solution for the environment and economy in the near future than the promotion of pure electric passenger vehicle technology. Methods The life cycle assessment (LCA) is used to assess the overall environmental impact of goods or services, including the entire life cycle of a product: extraction and processing of raw materials, manufacturing, distribution, use, reuse, maintenance, recycling, and final disposal [ 20 ][ 21 ] . LCA analysis can be performed based on two main approaches: process-based model and input-output based analysis [ 22 ] . Compared with the method of process-based model, the input-output analysis (IOA) can be applied to more macro level life cycle assessment. The Input-output analysis (IOA) can comprehensively include the direct and indirect contributions of all economic activities in an impact assessment, and it can be more easily adopted as a basis for assessing the impact of anticipated technological changes in a defined economic system [ 20 ] . With the ability to extend the analysis to the entire economic system, IOA-LCA allows for a more comprehensive assessment of environmental impacts, considering emissions that may be hidden in upstream production processes, which contributes to the reduction of emissions throughout the economic system [ 14 ] . The central formula of the input-output model is that for an economy consisting of \(n\) industries, total output \(X\) can be expressed as the sum of intermediate use \(AX\) and final demand (i.e., products that are no longer processed for production) \(Y\) in each industry: $$X=\text{AX}+Y$$ 1 $$X=(I-A{)}^{-1}Y$$ 2 Where, I is the identity matrix; \((\text{I-A}{)}^{-1}\) is the Leontief inverse matrix, also known as the complete demand factor; \(X\) is a N × N matrix. In order to investigate the difference between final production emissions and final demand emissions and the reasons for their formation, Skelton et al. [ 14 ] proposed a method for mapping embodied emission flows through the Leontief production system. This method is considered as an extension of the traditional SPA and can show a detailed flow analysis map of the supply chain between final production and consumption attribution. The SPA model has been widely used to identify key industrial sectors and supply chain pathways that lead to resource use and associated environmental impacts [ 23 ][ 24 ] , and is a suitable and effective method for quantifying demand-driven environmental emissions. An advantage of the Leontief model is the ability to track the intermediate purchasing chain through the layers of the production system triggered by the final demand. This is achieved by solving the Leontief inverse using its power series approximation, as follows [ 14 ] : $$L=(I-A{)}^{-1}=I+A+{A}^{2}+{A}^{3}+\text{...}+\text{proveded that li}{\text{m}}_{t\to {\infty }}{A}^{t}$$ 3 The relationship between each two production layers (PL) is as follows: $$\text{P}{\text{L}}^{t+1}=\text{P}{\text{L}}^{t}A$$ 4 Each sector relates to the environment through a number \(m\) of exogenous transactions (resources consumption or waste emissions), collected into the exogenous transactions coefficients vector \(\theta\) ,w, ich is a vector of direct emission coefficient, that is the amount of pollutant emissions generated per unit of economic output in each industry. \(\theta =E/X\) , where \(E\) is the direct pollutant emission vector for each sector and \(X\) is the total output vector for each sector. In this paper, we consider CO 2 and NOx emissions where the transportation sector is the main contributor [ 25 ] . The consumption of final products in each sector forms the final demand, which pulls upstream sectors along the production layer to produce intermediate products. Each sector generates direct emissions of pollutants from each production tier as it performs production activities at each production tier. Similarly, the consumption of intermediate goods by each production layer forms the reverse implied pollution flow from final production emissions back through each production layer to final demand emissions. The direct emissions \({D}^{t}\) , indirect production embodied emissions \({P}^{t}\) and consumption emissions \({E}^{t}\) for sector \(i\) at production layer 0 and production layer \(t\) are calculated as follows. Table 2 Direct, Consumption, and Production Attribution Equations for PL0 to PL3 Direct Consumption Production Final attribution at PL0 \({D}_{i}^{0}={\theta }_{i}{y}_{i}\) \({E}_{i}^{0}={m}_{i}{y}_{i}\) \({P}_{i}^{0}=\text{M*Y}\) Intermediate attribution at PL1 \({D}_{i}^{1}={\theta }_{i}\text{*A*Y}\) \({E}_{i}^{0}={m}_{i}\text{*A*Y}\) \({P}_{i}^{1}=\text{M*A*Y}\) Intermediate attribution at PL2 \({D}_{i}^{2}={\theta }_{i}\text{*}{\text{A}}^{2}\text{*Y}\) \({E}_{i}^{2}={m}_{i}\text{*}{\text{A}}^{2}\text{*Y}\) \({P}_{i}^{2}=\text{M*}{\text{A}}^{2}\text{*Y}\) Intermediate attribution at PL3 \({D}_{i}^{3}={\theta }_{i}\text{*}{\text{A}}^{3}\text{*Y}\) \({E}_{i}^{3}={m}_{i}\text{*}{\text{A}}^{3}\text{*Y}\) \({P}_{i}^{3}=\text{M*}{\text{A}}^{3}\text{*Y}\) Table 3 Embodied Emissions Flow Equations from sector at PL1 to sector at PL0 from sector at PL2 to sector at PL1 from sector at PL3 to sector at PL1 Embodied Emissions Flow \({E}_{\text{ij}}^{1\to 0}={m}_{i}\text{*}{\text{a}}_{\text{ij}}\text{*}{\text{y}}_{i}\) \({E}_{\text{ij}}^{2\to 1}={m}_{i}\text{*}{\text{a}}_{\text{ij}}\text{*A*Y}\) \({E}_{\text{ij}}^{3\to 2}={m}_{i}\text{*}{\text{a}}_{\text{ij}}\text{*}{\text{A}}^{2}\text{*Y}\) In Tables 2 and 3 , \(M\) is the N × N matrix of emission multipliers and \(m\) is the row vector (1×N) in \(M\) . \(M\) and \(m\) can be calculated according to Eqs. ( 5 ) and ( 6 ), respectively: $$M=\stackrel{\wedge }{\theta }L$$ 5 $$m={\theta }^{T}L$$ 6 Where \(\stackrel{\wedge }{\theta }\) is the diagonal form of the emission intensity and \({\theta }^{T}\) is the row vector form of the emission intensity. Finally, we should also note that the sum of the final production attributes equals the sum of the final demand attributes ( \({\sum }_{i=1}^{n}{P}_{i}^{0}={\sum }_{i=1}^{n}{E}_{i}^{0}\) ). In this paper, it is assumed that the supply relationship between sectors remains unchanged during the life cycle of the technology implementation (set to 15 years in this paper, based on the current end-of-life requirements for most vehicle models in China), and that the industry emission intensity and product prices remain constant. A similar hypothesis has been set in many studies [ 26 ][ 27 ][ 28 ] . The implementation of vehicle related emission reduction technologies requires additional investment costs, which is an important reference for countries or enterprises to adopt emission reduction strategies. The application of emission reduction technologies will cause changes in the final consumption. When the increased costs or benefits of applying energy-efficient technologies lead to changes in the final consumption of key sectors, the economic output of other sectors will be indirectly affected due to sectoral linkages, which will lead to changes in pollutant emissions in each sector. In order to obtain the net emission abatement potential of each technology, the direct emission reductions during the use stage and the indirectly induced emissions during the production stage are considered in this paper, using 2018 as the base year. Considered from the whole life cycle, the equipment and power used in these abatement technologies generate additional pollutant emissions during the production phase. In addition, the reduction in fuel consumption also reduces the pollutants emitted during production stages. The addition emissions in production stage generated through industry linkages, is denote as \(AD{E}_{production}\) . The top-down IOA method can be used to estimate the impact on the whole economic sectors from the final demand shock (reflected in monetary value) caused by the application of the technology, resulting in associated additional emissions in production stage. The SPA model introduced above can calculate the emissions in the production stage, and this method does not need too much micro production process data. We use the SPA model to estimate the additional emissions from the production phase of the technology, as described in Eqs. ( 3 )- ( 6 ) and Tables 2 and 3 , where it is important to note that when calculating the additional emissions of a technology, the variable \(Y\) is set to zero in all sectors except for the shock sector, which is a change in value (denoted as y 1 , y 2 and y 3 , in million of yuan ). The \({y}_{1}\) is the fuel cost, which is the final demand reduction in the refined oil sector in the IO table. The \({y}_{2}\) is the technology investment cost, which is the increase in final demand in the IO table for the automotive parts sector, rubber and related industries such as the complete vehicle manufacturing sector (see Table 4 ) due to the implementation of the motor vehicle retrofit technology. The \({y}_{3}\) is the electricity cost, which in the IO table is the increased final demand in the electricity sector due to the implementation of motor vehicle electrification technology. Here we set the fuel cost ( \({y}_{1}\) ) and electricity cost ( \({y}_{3}\) ) increase by a discount rate of 5% p.a. in the 15 year [ 11 ] , and the technology investment cost ( \({y}_{2}\) ) is completed in the first year, without considering discounting. The variation of these three final requirements could be estimated by the following formula. $${\text{y}}_{1}=\sum _{i=1}^{n}(\text{O}\text{P}\times \text{F}\text{E}\times \text{A}\text{M}\times \text{V}\text{O}\times \text{S}\times {10}^{-6}\times {\left(1+5\text{\%}\right)}^{n-1})$$ 7 $${\text{y}}_{2}=\text{I}\text{C}\times \text{V}\text{O}\times {10}^{-6}$$ 8 $${\text{y}}_{3}=\sum _{i=1}^{n}(\text{E}\text{P}\times \text{P}\text{M}\times \text{A}\text{M}\times \text{V}\text{O}\times {10}^{-6}\times {(1+5\text{\%})}^{n-1})$$ 9 In which, \(OP\) (yuan/L) is the oil price, and the average oil price in China in 2018 is 7.27 yuan /L for gasoline and 6.88 yuan/L for diesel [ 29 ] ; the variables \(FE\) , \(AM\) , \(VO\) and \(S\) are described in Eq. ( 7 ); \(IC\) (yuan) is the technology investment cost (see Table S1); \(EP\) (yuan/kW·h) is the electricity price, and the average feed-in tariff for power producers in China in 2018 is 0.374 yuan/kW·h [ 30 ] ; \(PM\) (kW·h/100km) is the power consumption per unit mileage (see Table S2); \(n\) is the year of technology use, ranging from 1 to 15. Table 4 Major industries where emission reduction technologies are causing \({\mathbf{y}}_{2}\) changes Technology Description Direct industries with increased final demand [ 31 ] Engine energy-saving technology Engine and transmission parts improvement Auto parts and accessories industry New energy technology Adopt new power systems such as electrification and hybrid power Automobile industry Drag reduction technology Low friction lubricant Lubricant improvement Refined petroleum and processed nuclear fuel products industry Low rolling resistance tire Rubber tire improvement Rubber products industry Lightweight Or Air resistance reduction Overall body design improvement Automobile industry For vehicles, the emission reduction caused by energy-saving or substitution technology could be accounted by the coefficient method of pollutant emission per unit of gasoline. Assuming no change in vehicle ownership is considered, the direct CO 2 abatement could be obtained by multiplying the CO 2 generation factor by the amount of fuel that would be saved by each type of technology in the next 15 years. Since NOx emissions are more likely to be influenced by tailpipe control technologies, the relationship between NOx production factor and final emission factor of fuel consumption is highly uncertain. In the absence of sufficient data, this article set the current emissions multiplied by the corresponding energy-saving efficiency of various technologies as the emissions of this technology. $${\text{A}\text{B}\text{E}}_{\text{u}\text{s}\text{e}-{\text{C}\text{O}}_{2}}\text{=α ×FE×VO×AM×S×ρ×1}{\text{0}}^{\text{-11}}\text{×15}$$ 10 $$\text{A}\text{B}{\text{E}}_{\text{u}\text{s}\text{e}-\text{N}\text{O}\text{x}}\text{=}\text{β}\text{×S×15}$$ 11 In which, the CO 2 and NOx direct abatement emissions are denoted as \({ABE}_{use-{CO}_{2}}\) and \(A{BE}_{use-NOx}\) , respectively; \(\alpha\) is the CO 2 generation factor, for gasoline is 2.93t/t and for diesel is 3.10t/t; \(FE\) is the fuel consumption per unit mileage(L/100km); \(VO\) is the current total ownership of vehicles; \(AM\) is the average annual mileage (km); \(\rho\) is the fuel density, 0.73 kg/L for gasoline and 0.84 kg/L for diesel; 15 is the service life of the vehicle; \(\beta\) is the annual statistical NOx emissions (Kt) of current total quantity of vehicles; \(S\) is the energy-saving efficiency in %. Detailed data for \(FE\) , \(VO\) , and \(AM\) are shown in Table S2, and detailed data for \(S\) are shown in Table S1. The potential for emission abatement over the whole life cycle is referred to as the net abatement potential/emissions, denoted as \({ABE}_{net}\) (CO 2 in Mt, NOx in Kt). The net abatement potential is the direct emission reductions from the use phase of the technology deducting the additional emissions generated in the production phase, which could be expressed as: $$\text{A}\text{B}{\text{E}}_{\text{n}\text{e}\text{t}}={\text{A}\text{B}\text{E}}_{\text{u}\text{s}\text{e}}-\text{A}\text{D}{\text{E}}_{\text{p}\text{r}\text{o}\text{d}\text{u}\text{c}\text{t}\text{i}\text{o}\text{n}}$$ 12 From the perspective of the ife cycle, the adoption of vehicle energy-saving or substitution technology will produce additional investment costs of technological transformation (above \({y}_{1}\) ). If electric vehicle technology is adopted, it will increase additional power costs (above \({y}_{2}\) ), but it will also save fuel and generate energy-saving benefits (above \({y}_{3}\) ). Economic benefits are the main driving force that may promote the long-term development of technology. We generally refer to emission reduction from the perspective of cost, however, but energy-saving technology may bring economic benefits. From the perspective of cost-benefit analysis, the net benefit ( \(NB\) ) is as follows. $$\text{N}\text{B}={\text{y}}_{3}-{\text{y}}_{1}-{\text{y}}_{2}$$ 13 The net abatement benefit per unit emissions ( \(AB\) , CO 2 unit yuan/t, NOx unit yuan/kg) could be calculated as follows: $$\text{A}\text{B}=\text{N}\text{B}/{\text{A}\text{B}\text{E}}_{\text{n}\text{e}\text{t}}$$ 14 Declarations Data availability The historical base period of this study is from 2012 to 2018. For the future emission reduction potential, this study takes 2018 as the base year and set the future research period as 15 ye The historical base period of this study is from 2012 to 2018. For the future emission reduction potential, this study takes 2018 as the base year and set the future research period as 15 years after 2018 (determined based on the current end-of-life requirements for most vehicle models in China[ 32 ] ). We organize the economic sectors in the original input-output tables for 2012 and 2018 into 142 sectors (139 sectors in the original table for 2012 and 153 sectors in the original table for 2018 of China) according to the latest industrial classification and coding requirements of the national economy[ 33 ] .The transportation sector included 11 subsectors: rail passenger transport, rail freight transport, road passenger transport, road freight transport, water passenger transport, water freight transport, air passenger transport, air freight transport, pipeline transport, multimodal transport, and postal service. rs after 2018 (determined based on the current end-of-life requirements for most vehicle models in China[ 32 ] ). We organize the economic sectors in the original input-output tables for 2012 and 2018 into 142 sectors (139 sectors in the original table for 2012 and 153 sectors in the original table for 2018 of China) according to the latest industrial classification and coding requirements of the national economy[ 33 ] .The transportation sector included 11 subsectors: rail passenger transport, rail freight transport, road passenger transport, road freight transport, water passenger transport, water freight transport, air passenger transport, air freight transport, pipeline transport, multimodal transport, and postal service. Since the transportation sector is not the main emission sector of sulfur dioxide and particulate matter, and considering the availability of data, NOx is only regarded as the representative of air pollutants in this paper. The NOx emissions data for each sector are from the National Bureau of Statistics of China, and NOx emissions of motor vehicles are provided by the Annual Report on Environmental Management of Mobile Sources in China (2019) [ 34 ] and converted to NOx emission reductions based on energy savings for each type of technology; CO 2 data for each sector of China are from CEADs (https://www.ceads.net.cn/data/), and CO 2 emission reduction data involving specific emission reduction technologies for motor vehicles are based on energy-related emission factors provided by the IPCC[ 35 ] , and CO 2 emission reductions are obtained based on the energy savings of each type of technology. For the future development of automotive emission reduction technology, the "New Energy Vehicle Industry Development Plan (2021-2035)" in China clearly states that pure electric vehicles, plug-in hybrid vehicles, fuel cell vehicle technology and common energy-saving technologies such as light weight and low frictional resistance are the future development direction. However, China lacks systematic and open literature on these technologies. Here, referring to the reports of the National Science Research Council and the National Highway Traffic Safety Administration in the U.S.A[ 36 ][ 37 ][ 38 ] and the research by Peng et al.[ 11 ] , this paper classifies emission reduction technologies into 3 categories: engine energy-saving technology, new energy technology, and drag reduction technology. Besides, 3 types of passenger cars, light trucks and heavy trucks are considered. Since upgrading exhaust gas control measures at this stage may lead to a slight increase in fuel consumption and CO 2 emissions, such measures are not involved in this paper. The names, energy efficiency and investment cost of each technical scenario and parameters of each type of automotive are shown in Table S1 and Table S2, respectively. References Zhang, R., Fujimori, S., & Hanaoka, T. The contribution of transport policies to the mitigation potential and cost of 2 C and 1.5 C goals. Environ. Res. Lett.13(5), 054008(2018). IEA, 2019a. World Energy Balance 2019. China; Second National Pollution Source Census Bulletin; Ministry of Ecology and Environment of the People's Republic of China, National Bureau of Statistics, Ministry of Agriculture and Rural Affairs of the People's Republic of China,2020. Wu X., Wu Y., & Zhang S., et al. Assessment of vehicle emission programs in China during 1998–2013: Achievement, challenges and implications. Environ. Pollut. 214: 556–567(2016). Wu Y., Zhang S., & Li M. et al. The challenge to NOx emission control for heavy-duty diesel vehicles in China. Atmos. Chem. Phys.12(19): 9365–9379 (2012). Liu, H., Huang, F., & Deng, F. et al. Road freight emission in China: From supply chain perspective. Environ Pollut. 285, 117511(2021). Du, H., Chen, Z., & Peng, B. et al. What drives CO2 emissions from the transport sector? A linkage analysis. Energy. 175, 195–204(2019). Yu, Y., Li, S., & Sun, H. et al. Energy carbon emission reduction of China’s transportation sector: an input–output approach. Econ. Anal. Policy.69, 378–393(2021). Lutsey, N., Meszler, D., & Isenstadt, A. et al. Efficiency technology and cost assessment for US 2025–2030 light-duty vehicles. International Council on Clean Transportation (ICCT), Washington, USA, 2017. Norris J, & Escher G. Heavy duty vehicles technology potential and cost study. The International Council on Clean Transportation, 2017. Peng, B., Fan, Y., & Xu, J. Integrated assessment of energy efficiency technologies and CO2 abatement cost curves in China’s road passenger car sector. Energy Convers. Manag. 109: 195–212(2016). Rosenfeld, D. C., Lindorfer, J., & Fazeni-Fraisl, K. Comparison of advanced fuels—which technology can win from the life cycle perspective?. J. Clean. Prod. 238: 117879 (2019). Zhu, Y., Skerlos, S., & Xu, M. et al. Reducing Greenhouse Gas Emissions from US Light-Duty Transport in Line with the 2°C Target. Environ. Sci. Technol. 55(13): 9326–9338(2021). Motor vehicles mandatory scrapping standards regulations. Ministry of Commerce, Development and Reform Commission, Ministry of Public Security, Ministry of Environmental Protection Order No. 12, 2013. National Bureau of Statistics Website. China;Annual Report on Environmental Management of Motor Vehicles in China; Ministry of Ecology and Environment of the People's Republic of China.(2019). IPCC (Intergovernmental Panel on Climate Change). Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories; IPCC: Geneva, Switzerland. (2019). National Research Council. Assessment of fuel economy technologies for light-duty vehicles. National Academies Press. (2011). National Research Council. Technologies and approaches to reducing the fuel consumption of medium-and heavy-duty vehicles. National Academies Press. (2010). National Highway and Traffic Safety Administration (NHTSA). Preliminary regulatory impact analysis: corporate average fuel economy for MY 2011–2015. Passenger cars and light trucks. U.S. Department of Transportation. Washington, D.C., U.S. (2008). Rocco, M. V., Casalegno, A., & Colombo, E. Modelling road transport technologies in future scenarios: Theoretical comparison and application of Well-to-Wheels and Input-Output analyses. Appl. Energy. 232: 583–597(2018). Guinée, J.B. Handbook on life cycle assessment operational guide to the ISO standards. Int J Life Cycle Assess; pp. 311–313(2002). Garrett, J., Hendrickson, C. T., & Horvath, A. et al. Electron. General Purpose Computer-Aided Engineering Tools for Environmental Software Systems. In Environmental Software Systems, pp. 176–181(1997). Skelton, A., Guan, D., & Peters, G. P. et al. Mapping flows of embodied emissions in the global production system. Environ. Sci. Technol. 45(24): 10516–10523(2011). Hanaka, T., Kagawa, S., & Ono, H. et al. Finding environmentally critical transmission sectors, transactions, and paths in global supply chain networks. Energy Econ. 68: 44–52(2017). He, K., Mi, Z., & Chen, L. et al. Critical transmission sectors in embodied atmospheric mercury emission network in China. J Ind Ecol. 25(6): 1644–1656 (2021). Suh, S. Functions, commodities and environmental impacts in an ecological–economic model. Ecol Econ. 48(4): 451–467(2004). Economic evaluation of sectoral emissionreduction objectives for climate change – Comparison of top-down and bottom-upanalysis of emission reduction opportunities for CO2 in the European Union.. Final Report. Greece. (2001). Economic evaluation of emissions reductions in the transport sector of the EU: bottom-up analysis. Final Report. (2001). Cost-effectiveness of greenhouse gases emission reductions in various sectors. Order of the European Commission; Zürich/Bern.(2007). Oil Price Website; http://youjia.chemcp.com/index.asp . National Energy Administration; http://www.nea.gov.cn/2019-11/05/c_138530 255.htm . National Bureau of Statistics Website; http://www.stats.gov.cn/xxgk/tjbz/gjtjbz/ 201905 /t20190521_1758938.html . Notice on the financial subsidy policy for the promotion and application of new energy vehicles in 2022. Caijian [2021] No. 466.(2021). Zhao, D., Lei, Y., & Zhang, Y. et al. Analysis of vehicular CO2 emission in the Central Plains of China and its driving forces. Sci. Total Environ. 152758(2022). Xu, Y., Liu, Z., & Xue, W. et al. Identification of on-road vehicle CO2 emission pattern in China: A study based on a high-resolution emission inventory. Resour Conserv Recycl. 175: 105891(2021). Hu, J., Li, J., & Hu, Z. et al. Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming. Energy. 215: 118851(2021). Silver, F., & Brotherton, T. CalHEAT research and market transformation roadmap for medium-and heavy-duty trucks. Pasedena, CA: California Hybrid, Efficient and Advanced Truck Research Center.(2013). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1508775","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":100688043,"identity":"78e286d3-076b-40d6-87c6-e1274e1304cb","order_by":0,"name":"Yuan Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Wang","suffix":""},{"id":100688044,"identity":"3a930ab1-f13c-49c3-9700-c416e01713af","order_by":1,"name":"Liying Ping","email":"","orcid":"","institution":"Tianjin University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liying","middleName":"","lastName":"Ping","suffix":""},{"id":100688045,"identity":"4cd1a772-776c-48a2-ac32-844ba3abdbfc","order_by":2,"name":"Lien-Chieh Lee","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lien-Chieh","middleName":"","lastName":"Lee","suffix":""},{"id":100688046,"identity":"db45d0ef-ecfa-40e6-a542-82c2ed766e40","order_by":3,"name":"Hongyu Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Zhang","suffix":""},{"id":100688047,"identity":"46153428-4f4f-4a1c-b375-3382ebe33902","order_by":4,"name":"Binbin Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3RsQqCQBzH8b8cXIs4Xxj1CieCIfUwJ0GThRC0JgROtl9vUW9gOLRY8w1BRdBSQ+DQ0hBKrldtQffdfnAf+MMBqFQ/GgIKDANKgBQz+ZxgL/yCADAA3QrhE9KuzdJLEOyGhhnfD24ETUMwLQ8kxI23/Q6n5xFubJZhPQK7LhgyuYRQ4Tu2TlMvIoOSeAvBMNJlZH+tiH8syOQ9Ebp9ehGtIIy+I27sO4jTdIRJ3+JkS6x5dpyaMtKuZXYePNJhi/cOORl3W8a6t8qlhwFgUg1Eyv/XQgkoCbpVQ7vJnqpUKtXf9gST6EtH5kYDqAAAAABJRU5ErkJggg==","orcid":"","institution":"Tianjin University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Binbin","middleName":"","lastName":"Peng","suffix":""},{"id":100688048,"identity":"76fa3620-1e13-4af6-ae96-a6eebc8b7662","order_by":5,"name":"Zhou Pan","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Pan","suffix":""},{"id":100688049,"identity":"acc48ab4-2007-4c2b-955b-a1b6fd00261f","order_by":6,"name":"Chenbo Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chenbo","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2022-03-31 10:45:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1508775/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1508775/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":20797894,"identity":"30fc6666-958b-4d8f-92d8-95b18921055b","added_by":"auto","created_at":"2022-04-26 19:33:15","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":447447,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in CO\u003csub\u003e2\u003c/sub\u003e(a) and NOx(b) emissions in the transportation sector and their drivers.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1508775/v1/19a852dc81f6c704d21f33ba.jpeg"},{"id":20797169,"identity":"831bdb9e-29de-4a63-8c57-be123e0aded9","added_by":"auto","created_at":"2022-04-26 19:23:15","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3203767,"visible":true,"origin":"","legend":"\u003cp\u003eMap of emission flows of China's transportation sector.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1508775/v1/68bed9514ec608a5f8867d4c.jpeg"},{"id":20797170,"identity":"8685d605-139e-4603-917c-358d59ed5977","added_by":"auto","created_at":"2022-04-26 19:23:15","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6446934,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEmissions of equipment and energy required for PC12 during the production phase. \u003c/strong\u003e(a) Additional CO\u003csub\u003e2 \u003c/sub\u003eemissions in the production phase of electric vehicles; (b) Additional NOx emissions in the production phase of electric vehicles; (c) Additional CO\u003csub\u003e2\u003c/sub\u003e emissions in the production phase of electricity; (d) Additional NOx emissions in the production phase of electricity; (e)Reduced CO\u003csub\u003e2\u003c/sub\u003e emissions in the production phase of fuel oil; (f) Reduced NOx emissions in the production phase of fuel oil.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1508775/v1/c068944103ff23b8ec4a17a4.jpeg"},{"id":20797165,"identity":"02b8c008-6690-4904-9eee-0dd0a9ba7ae8","added_by":"auto","created_at":"2022-04-26 19:23:15","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4463637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e (a) and NOx (b) abatement potential over the full life cycle of each technology. \u003c/strong\u003eYellow, green, orange and purple boxes indicate vehicle's emission reductions during the use phase, emission reductions in the fuel use, additional emissions from the manufacturing of equipment during the production phase and additional emissions from electricity during the production phase, respectively. \u003cstrong\u003e(c) Synergy of CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e and NOx reduction for each technology. \u003c/strong\u003eThe specific standardized methods and quantitative evaluation of synergistic effect are shown in Supplementary Information (section 1.3 and Table S3). \u003cstrong\u003e(d) Abatement benefits of each technology.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1508775/v1/26a45fbd719d69b79a8b3b3a.jpeg"},{"id":20797403,"identity":"99aadd90-aa13-4c37-beb8-00b3f49b620d","added_by":"auto","created_at":"2022-04-26 19:28:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68436,"visible":true,"origin":"","legend":"\u003cp\u003eThe framework of net emission abatement potential based on LCA perspective.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-1508775/v1/f36fce6e844a2a0e400388b6.png"},{"id":21903400,"identity":"0f6cc439-a3c8-4400-8540-6c1320ade305","added_by":"auto","created_at":"2022-05-26 09:05:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1273554,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1508775/v1/74a27ffa-60d1-406b-acad-6a6cccacb2ba.pdf"},{"id":20797168,"identity":"f03dafdb-d854-4ef6-b858-ca08ea32b8d6","added_by":"auto","created_at":"2022-04-26 19:23:15","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":54007,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation2.docx","url":"https://assets-eu.researchsquare.com/files/rs-1508775/v1/c40d88d81d8b2a81dceb0641.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Finding the best approach for the transportation sector to synergistically reduce CO2 and air pollutants emissions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe transportation sector plays an important role in the economic activities of society by linking production, exchange, distribution and consumption. Meanwhile, the transportation sector is a major source of CO\u003csub\u003e2\u003c/sub\u003e and air pollutants emissions\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, with road subsector making the largest contribution\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The China's latest national plan (14th Five-Year Plan) puts forward the requirement of synergy in carbon and air pollutants reduction, and the transportation sector has a significant impact on achieving this goal. China has now become very stringent in managing transportation emissions, especially in vehicles. A range of direct and enforceable end-of-pipe treatment policies, such as improved fuel quality and stricter tailpipe emission standards, have achieved significant emission reductions\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, vehicle emissions remain high as transportation demand increases.\u003c/p\u003e \u003cp\u003eTransportation is the demand generated by economic activities. The analysis of emissions from the transportation sector inevitably involves all sectors of the economy. Therefore, it is important to explore the economic relationship between the transportation sector and other sectors, which requires extremely comprehensive data. The input-output table is the most appropriate tool because it is able to link the production of goods to the exchange of materials between economic sectors. The input-output analysis have shown that heavy industry, electricity, construction and services are the main sectors related to air pollutants and CO\u003csub\u003e2\u003c/sub\u003e emissions from the transport sector\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The final demand is the strongest driver of the rapid growth of CO\u003csub\u003e2\u003c/sub\u003e emissions from the transportation sector\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. While these studies have contributed significantly to the understanding of transportation emissions and have generated useful production and demand-side policy recommendations. However, facing the prominent position of transportation emission, it is not enough to rely on macro level analysis alone. Research on specific abatement technologies for the transportation sector, especially the road subsector, is essential.\u003c/p\u003e \u003cp\u003eSeveral studies have assessed the abatement potential and costs of vehicle emission abatement technologies at the micro-level. For example, International Council on Clean Transportation (ICCT) studied the abatement potential and cost of vehicles over the time period 2015 to 2030\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. It found that the investment cost of vehicle abatement technology rises as abatement rates increase, but consumer savings in fuel costs will be two to three times the cost of vehicle technology investments by 2030. Peng et al. \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e studied 55 passenger car abatement technologies in China from 2010 to 2030, with abatement costs ranging from \u0026minus;\u0026thinsp;1324.59 to 7623.47 yuan/t from 2010 to 2030, with a cumulative CO\u003csub\u003e2\u003c/sub\u003e abatement potential of about 27Mt.\u003c/p\u003e \u003cp\u003eHowever, these studies mainly analyzed the potential and costs of energy-efficient technologies in the use phase, ignoring the emissions of these technologies in the production phase.. New energy technologies have high CO\u003csub\u003e2\u003c/sub\u003e emissions in the production phase, which can reach up to 50% of the emissions reductions in the use phase\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In the coming decades, production emissions will account for an increasing share of new energy vehicle emissions. Meanwhile, the response of global temperature to the impulse of emitted greenhouse gases is an important factor in taking productive emissions into account. The temperature impulse response of global temperature has a lag and will peak in a few decades. Production emissions now will affect temperatures decades into the future\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Inadequately, the above study only measured the emissions of new energy vehicles, such as hybrid and electric vehicles, at the production stage, without fully analyzing the emissions of the whole transportation industry and comparing more abatement technologies at the production stage.\u003c/p\u003e \u003cp\u003eBased on the above considerations, this paper integrates life cycle assessment based on input-output analysis (IOA-LCA) and structural path analysis (SPA) to trace the CO\u003csub\u003e2\u003c/sub\u003e and air pollutants emission streams hidden in the supply chain of the whole transportation sector and 33 combination scenarios of vehicle types and technologies from a life-cycle perspective, including production phase and use phase. This innovation is that the additional emissions generated by all vehicle abatement technologies at the equipment manufacturing and energy production stages are quantitatively assessed, and the net abatement effect of each technology at the whole economic system level is evaluated. Combined with the net abatement potential and economy cost-benefit analysis, the future motor vehicle abatement technologies for the synergistic reduction of air pollutants and CO\u003csub\u003e2\u003c/sub\u003e in China are proposed in this paper.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eChange and driving force analysis of CO\u003csub\u003e2\u003c/sub\u003e and NOx emissions in the transportation sector\u003c/h2\u003e \u003cp\u003eAccording to the CO\u003csub\u003e2\u003c/sub\u003e and NOx emissions of 11 subsectors of transportation over the period 2012\u0026ndash;2018, we find CO\u003csub\u003e2\u003c/sub\u003e emissions from the transportation sector increased substantially while NOx emissions remained essentially unchanged, and the road freight contributes the most to the increase of CO\u003csub\u003e2\u003c/sub\u003e emissions, accounting for about 40%, followed by the multimodal transport subsector and the road passenger transport subsector (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, we apply the structural decomposition analysis (SDA) method to analyze the effects of emission intensity, industrial structure, and final demand on the drivers of changes in CO\u003csub\u003e2\u003c/sub\u003e and NOx emissions. The overall SDA calculation method can be seen in Supporting Information section 1.2. The factors causing the rapid increase in CO\u003csub\u003e2\u003c/sub\u003e emissions are the simultaneous increase in both emission intensity and final demand. The reduction of NOx emission intensity offsets the increase of final demand, and the impact of industrial structure on CO\u003csub\u003e2\u003c/sub\u003e and NOx is not significant. The reasons for the above phenomenon are related to environmental protection policies. From 2013 to 2018, China introduced a series of mobile source emission reduction measures to reduce NOx emissions, which achieved significant emission reduction effects\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, China has not issued standards or policies for CO\u003csub\u003e2\u003c/sub\u003e emission reduction of motor vehicles, resulting in high and rapid growth of CO\u003csub\u003e2\u003c/sub\u003e emissions in the transportation sector.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEmission flows between consumption and production\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eEmission flows of transportation sector\u003c/h2\u003e \u003cp\u003eTo explore more systematically the emissions of transportation sector in its production stage, we use the modified SPA method to provide an exhaustive diagram of the supply chain linkages related to the transport final consumption attribution and final production attribution, highlighting the emissions implicit in the intermediate product streams that connect different economic sectors along the supply chain.\u003c/p\u003e\u003cp\u003eFrom the perspective of final consumption attribution, the emissions include the direct emissions and embodied emission flows \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the share of direct emissions from transportation is very high. The direct emissions of CO\u003csub\u003e2\u003c/sub\u003e and NOx account for 50% and 81% of all emissions, respectively. Regarding the embodied CO\u003csub\u003e2\u003c/sub\u003e emission flows, the electricity is significant in each production layer. For other sectors, the story is quite different. Refined oil and auto parts sectors are significant in production layer PL1 (accounting for 28% in the total embodied emissions flows), while the mining, metal products, and equipment manufacturing sectors are significant in PL2 and PL3 (accounting for 35% respectively). For NOx, both the direct emissions and the embodied emissions from transportation are dominant in all production layers, accounting for 65%, 40%, and 34% of the total embodied emissions in PL1, PL2, and PL3, respectively.\u003c/p\u003e \u003cp\u003eFrom the aspect of final production, the CO\u003csub\u003e2\u003c/sub\u003e and NOx emissions due to transportation during production are very high (leftmost column of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This is similar to the findings revealed in the road freight study by Liu et al. \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, CO\u003csub\u003e2\u003c/sub\u003e emissions from China's transportation sector do not decrease but increase, while the NOx emissions only decrease slightly. Therefore, it is not enough to focus only on the industry sector scale, and a more detailed analysis of the whole life cycle of the technical path is needed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEmission flows of typical technologies\u003c/h2\u003e \u003cp\u003eIn order to analyze the additional emissions generated by motor vehicle emission reduction technologies, we calculate the emission flows caused by the consumption of intermediates in the production process of 33 combination scenarios of vehicle types and technologies, and the comparative analysis of each technology is presented in section 3.3.1 below. We take the example of the purely electric technology for passenger cars scenario (PC12), which had the highest production costs and the most complex production process. By using the SPA method, we quantitatively analyze the emission flows caused by this technology, including additional emissions from electric vehicle manufacturing and power production, as well as additionally reduced emissions from fuel production (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDifferent from the results of analyzing the whole transportation sector with SPA method, the auto parts and steel rolling sector has also become a prominent contributor to emission flows in the SPA results of PC12 scenario alone, which is responsible for approximately 50% of all CO\u003csub\u003e2\u003c/sub\u003e embodied emissions and 40% of all NOx embodied emissions in the PL1, PL2 and PL3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a)~(b)). The production of electricity and fuel oil is different from the complex motor vehicle manufacturing industry, with simple intermediate production links (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(c)~(f)).\u003c/p\u003e \u003cp\u003eThe result shows that if all existing passenger cars are converted to electric vehicles, although it will save emissions by 470.44 Mt CO\u003csub\u003e2\u003c/sub\u003e and 648.28 Kt NOx from fuel oil production, it will cause an additional emission about 494.44 Mt CO\u003csub\u003e2\u003c/sub\u003e and 676.13 Kt NOx in the electric vehicles manufacturing phase. These two parts almost completely offset each other. Moreover, the electric vehicles require about 216.33 Mt CO\u003csub\u003e2\u003c/sub\u003e and 80.39 Kt NOx emissions from additional electric power production. Therefore, the low carbon of power production is the key to the emission reduction of electric vehicle technology. At the same time, it should also be taken into account that due to fuel oil conservation, emissions in the production stage are reduced, offsetting the additional emissions generated by the manufacture of electric vehicles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of emission abatement potential and costs of typical vehicle energy-saving technologies\u003c/h2\u003e \u003cp\u003eIn order to analyze the net actual impact of the implementation of typical energy-saving technologies for vehicles on pollutant emissions, we comprehensively consider the emissions in the use stage and production stage, and analyze the engine energy-saving technology, new energy technology and drag reduction technology of passenger cars, light trucks and heavy trucks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of emission abatement potential over the whole life cycle\u003c/h2\u003e \u003cp\u003e(1) Emission reductions in the production and use stage of each technology\u003c/p\u003e \u003cp\u003eBased on the above, the SPA method is applied to calculate the emissions in the production stage of all 33 combination scenarios of vehicle types and technologies. The results show that the additional emissions resulting from the production phase of all technologies are smaller than emission reductions from their use phase, indicating that all 33 combination scenarios of vehicle types and technologies could achieve net emission reductions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In terms of vehicle types, passenger cars and heavy trucks have greater potential of CO\u003csub\u003e2\u003c/sub\u003e and NOx emission reductions. Regarding technologies, new energy technology (T-Ⅱ) has the highest emission reduction potential. It is worth noting that the hybrid and pure electrification technologies have higher CO\u003csub\u003e2\u003c/sub\u003e emissions in the production phase than others. Especially, for passenger car plug-in hybrid (PC11) and passenger car pure electric (PC12) technologies, the CO\u003csub\u003e2\u003c/sub\u003e emission of vehicle production could reach 50% of the emission reduction in the use stage. Unlike CO\u003csub\u003e2\u003c/sub\u003e, it could be found that the additional NOx emissions caused by each type of technology in their production phase are small, and the emission reduction in the use phase plays a dominant role in the net emission abatement (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(b)).\u003c/p\u003e \u003cp\u003eAlthough there are differences in the main emission stages, these technologies with high CO\u003csub\u003e2\u003c/sub\u003e reduction potential would also have high reduction potential for NOx. Especially, the new energy technologies have the best synergistic effect on reducing both CO\u003csub\u003e2\u003c/sub\u003e and NOx. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(c) and Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eNet benefit analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(d) shows the net benefits (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(NB\\)\u003c/span\u003e\u003c/span\u003e) of the 33 vehicle types and technology combination options. It could be found that most technologies are cost-effective. The economic benefits of fuel saving in the use stage are greater than the additional invest costs of energy-saving technologies, except for passenger car plug-in hybrid technology (PC11) and passenger car pure electric technology (PC12). The PC11 and PC12 technology have negative net benefits because their additional investment costs and electricity costs exceed the fuel savings benefits. According to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the drag reduction technologies category has the highest net abatement benefit of CO\u003csub\u003e2\u003c/sub\u003e on average. For NOx, however, the engine energy-saving technology category has the highest net abatement benefit on average. New energy technology has the lowest net benefit per unit reduction of CO\u003csub\u003e2\u003c/sub\u003e and NOx (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For instance, from the whole life cycle perspective, the costs of PC12 technology are the highest and the net abatement benefits per unit of CO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003eX\u003c/sub\u003e are \u0026minus;\u0026thinsp;1424 yuan/t and \u0026minus;\u0026thinsp;75 yuan/kg, respectively. However, according to the above analysis of abatement potential, compared with other technologies, the PC12 technology scenario has the greatest emission reduction potential. Therefore, the promotion of this technology requires additional long term massive subsidies. However, according to the latest national energy policy, China will no longer give subsidies for the purchase of new electric vehicles on December 31, 2023\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The withdrawal of fuel vehicles may be a mandatory policy in the future. Therefore, the costs would be transferred to consumers. We notice that heavy truck parallel hybrid technology scenario (HT3) also has a large emission reduction potential, with the highest CO\u003csub\u003e2\u003c/sub\u003e reduction potential and the second highest NOx reduction potential after PC12 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a) - (b)), but the HT3 technology scenario has the highest net benefit among all technologies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(d)). Therefore, this technology is cost-effective and worth popularizing. The net abatement benefits per unit emissions (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(AB\\)\u003c/span\u003e\u003c/span\u003e) for each technology are shown in Supplementary Information Section 3 and Table S5.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage net abatement benefit per unit for each type of technology\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAverage net abatement benefit per unit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eEngine energy-saving technology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eNew energy technology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eDrag reduction technology\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e (yuan/t)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2,165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2,056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2,019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOx (yuan/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1,533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote: PC is passenger car, LT is light truck, HT is heavy truck.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current emission reduction measures of transportation are weak for CO\u003csub\u003e2\u003c/sub\u003e control. CO\u003csub\u003e2\u003c/sub\u003e emissions from the transportation sector have increased rapidly, from 317.6 Mt in 2012 to 746.2 Mt in 2018. In future, the transportation sector will be critical for the entire economic system to achieve the goal of reducing pollutants and carbon. The transportation policies of China should focus more on the coordinated emission reduction of CO\u003csub\u003e2\u003c/sub\u003e and air pollutants.\u003c/p\u003e \u003cp\u003eTo examine the relationship between the transportation sector and other sectors, we use the SPA method to make a detailed profile of production emissions due to the transportation sector, including direct and embodied emissions. The transportation sector causes much higher direct emissions than embodied emissions. Direct emissions of CO\u003csub\u003e2\u003c/sub\u003e and NOx from the production phase account for 50% and 81% of all emissions, respectively. This is because the direct emissions in most production phase are related to road freight\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The transportation industry itself will form a vicious circle, that is, the increase of the scale of the transportation industry will drive the development of electricity, refined petroleum, auto parts, mining, equipment manufacturing, and metal products industries, and these industries need more transportation support for their production, resulting in more emissions. Therefore, it is suggested to formulate low-carbon policies and plans for vehicles and their power production stages. Special attention should be paid to the freight emissions caused by the production stage.\u003c/p\u003e \u003cp\u003eFrom a more micro technical point of view, 33 vehicle abatement technology scenarios are analyzed in this paper. Most of them have good synergistic CO\u003csub\u003e2\u003c/sub\u003e and NOx reduction effects. In addition, we find that all technology scenarios, except for pure electric (PC12) and plug-in hybrid (PC11) technologies for passenger cars, have net benefits due to fossil fuel savings. Notably, the passenger car pure electric technology (PC12) has the highest potential for emission reduction, but also requires the highest cost. Another noteworthy point is that the parallel hybrid technology for heavy trucks (HT3) has the greatest potential to reduce emissions, which is consistent with the findings of Zhao et al.\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e and Xu et al.\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Moreover, this technology has the largest net benefit. In terms of technical principle, the parallel hybrid technology of heavy truck is modified based on the traditional fuel vehicle, and its key is dual engine and electronic auxiliary unit\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Therefore, the modification cost is low. The pure electric trucks cannot meet the long-distance transportation requirements, while the pollution emission of traditional diesel power trucks is too high. The parallel hybrid technology for trucks can make up for the shortcomings of diesel power and pure electric technology, and meet the needs of transportation and emission reduction at the same time. Currently, heavy trucks using parallel hybrid technology are more common in Europe\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, while they are rare in China. The reason is that in China hybrid heavy trucks do not have the same financial subsidies as pure electric passenger cars, resulting in low enthusiasm for hybrid heavy trucks production. Combined with the above research conclusions on the important role of freight transportation in the production stage, it is suggested to formulate policies to promote parallel hybrid technology for heavy trucks. Under the background of China's national strategy of green freight and optimization of traffic structure, the promotion of hybrid heavy trucks may be a more win-win solution for the environment and economy in the near future than the promotion of pure electric passenger vehicle technology.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe life cycle assessment (LCA) is used to assess the overall environmental impact of goods or services, including the entire life cycle of a product: extraction and processing of raw materials, manufacturing, distribution, use, reuse, maintenance, recycling, and final disposal\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. LCA analysis can be performed based on two main approaches: process-based model and input-output based analysis \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Compared with the method of process-based model, the input-output analysis (IOA) can be applied to more macro level life cycle assessment. The Input-output analysis (IOA) can comprehensively include the direct and indirect contributions of all economic activities in an impact assessment, and it can be more easily adopted as a basis for assessing the impact of anticipated technological changes in a defined economic system\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. With the ability to extend the analysis to the entire economic system, IOA-LCA allows for a more comprehensive assessment of environmental impacts, considering emissions that may be hidden in upstream production processes, which contributes to the reduction of emissions throughout the economic system\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe central formula of the input-output model is that for an economy consisting of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\\)\u003c/span\u003e\u003c/span\u003e industries, total output \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(X\\)\u003c/span\u003e\u003c/span\u003e can be expressed as the sum of intermediate use \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(AX\\)\u003c/span\u003e\u003c/span\u003e and final demand (i.e., products that are no longer processed for production) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Y\\)\u003c/span\u003e\u003c/span\u003e in each industry:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$X=\\text{AX}+Y$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$X=(I-A{)}^{-1}Y$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, I is the identity matrix; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\text{I-A}{)}^{-1}\\)\u003c/span\u003e\u003c/span\u003e is the Leontief inverse matrix, also known as the complete demand factor; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(X\\)\u003c/span\u003e\u003c/span\u003e is a N \u0026times; N matrix.\u003c/p\u003e \u003cp\u003eIn order to investigate the difference between final production emissions and final demand emissions and the reasons for their formation, Skelton et al.\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e proposed a method for mapping embodied emission flows through the Leontief production system. This method is considered as an extension of the traditional SPA and can show a detailed flow analysis map of the supply chain between final production and consumption attribution. The SPA model has been widely used to identify key industrial sectors and supply chain pathways that lead to resource use and associated environmental impacts\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e][\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, and is a suitable and effective method for quantifying demand-driven environmental emissions.\u003c/p\u003e \u003cp\u003eAn advantage of the Leontief model is the ability to track the intermediate purchasing chain through the layers of the production system triggered by the final demand. This is achieved by solving the Leontief inverse using its power series approximation, as follows \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$L=(I-A{)}^{-1}=I+A+{A}^{2}+{A}^{3}+\\text{...}+\\text{proveded that li}{\\text{m}}_{t\\to {\\infty }}{A}^{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe relationship between each two production layers (PL) is as follows:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\text{P}{\\text{L}}^{t+1}=\\text{P}{\\text{L}}^{t}A$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEach sector relates to the environment through a number \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(m\\)\u003c/span\u003e\u003c/span\u003e of exogenous transactions (resources consumption or waste emissions), collected into the exogenous transactions coefficients vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\theta\\)\u003c/span\u003e\u003c/span\u003e,w, ich is a vector of direct emission coefficient, that is the amount of pollutant emissions generated per unit of economic output in each industry.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\theta =E/X\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(E\\)\u003c/span\u003e\u003c/span\u003e is the direct pollutant emission vector for each sector and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(X\\)\u003c/span\u003e\u003c/span\u003e is the total output vector for each sector. In this paper, we consider CO\u003csub\u003e2\u003c/sub\u003e and NOx emissions where the transportation sector is the main contributor\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe consumption of final products in each sector forms the final demand, which pulls upstream sectors along the production layer to produce intermediate products. Each sector generates direct emissions of pollutants from each production tier as it performs production activities at each production tier. Similarly, the consumption of intermediate goods by each production layer forms the reverse implied pollution flow from final production emissions back through each production layer to final demand emissions. The direct emissions \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}^{t}\\)\u003c/span\u003e\u003c/span\u003e, indirect production embodied emissions \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}^{t}\\)\u003c/span\u003e\u003c/span\u003e and consumption emissions \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}^{t}\\)\u003c/span\u003e\u003c/span\u003e for sector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e at production layer 0 and production layer \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e are calculated as follows.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect, Consumption, and Production Attribution Equations for PL0 to PL3\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConsumption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProduction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinal attribution at PL0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{i}^{0}={\\theta }_{i}{y}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{i}^{0}={m}_{i}{y}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{i}^{0}=\\text{M*Y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate attribution at PL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{i}^{1}={\\theta }_{i}\\text{*A*Y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{i}^{0}={m}_{i}\\text{*A*Y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{i}^{1}=\\text{M*A*Y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate attribution at PL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{i}^{2}={\\theta }_{i}\\text{*}{\\text{A}}^{2}\\text{*Y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{i}^{2}={m}_{i}\\text{*}{\\text{A}}^{2}\\text{*Y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{i}^{2}=\\text{M*}{\\text{A}}^{2}\\text{*Y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate attribution at PL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{i}^{3}={\\theta }_{i}\\text{*}{\\text{A}}^{3}\\text{*Y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{i}^{3}={m}_{i}\\text{*}{\\text{A}}^{3}\\text{*Y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{i}^{3}=\\text{M*}{\\text{A}}^{3}\\text{*Y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEmbodied Emissions Flow Equations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003efrom sector at PL1 to sector at PL0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003efrom sector at PL2 to sector at PL1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003efrom sector at PL3 to sector at PL1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmbodied Emissions Flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{\\text{ij}}^{1\\to 0}={m}_{i}\\text{*}{\\text{a}}_{\\text{ij}}\\text{*}{\\text{y}}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{\\text{ij}}^{2\\to 1}={m}_{i}\\text{*}{\\text{a}}_{\\text{ij}}\\text{*A*Y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{\\text{ij}}^{3\\to 2}={m}_{i}\\text{*}{\\text{a}}_{\\text{ij}}\\text{*}{\\text{A}}^{2}\\text{*Y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(M\\)\u003c/span\u003e\u003c/span\u003e is the N \u0026times; N matrix of emission multipliers and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(m\\)\u003c/span\u003e\u003c/span\u003e is the row vector (1\u0026times;N) in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(M\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(M\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(m\\)\u003c/span\u003e\u003c/span\u003e can be calculated according to Eqs.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and (\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), respectively:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$M=\\stackrel{\\wedge }{\\theta }L$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$m={\\theta }^{T}L$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{\\wedge }{\\theta }\\)\u003c/span\u003e\u003c/span\u003e is the diagonal form of the emission intensity and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta }^{T}\\)\u003c/span\u003e\u003c/span\u003e is the row vector form of the emission intensity.\u003c/p\u003e \u003cp\u003eFinally, we should also note that the sum of the final production attributes equals the sum of the final demand attributes (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{i=1}^{n}{P}_{i}^{0}={\\sum }_{i=1}^{n}{E}_{i}^{0}\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this paper, it is assumed that the supply relationship between sectors remains unchanged during the life cycle of the technology implementation (set to 15 years in this paper, based on the current end-of-life requirements for most vehicle models in China), and that the industry emission intensity and product prices remain constant. A similar hypothesis has been set in many studies\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e][\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe implementation of vehicle related emission reduction technologies requires additional investment costs, which is an important reference for countries or enterprises to adopt emission reduction strategies. The application of emission reduction technologies will cause changes in the final consumption. When the increased costs or benefits of applying energy-efficient technologies lead to changes in the final consumption of key sectors, the economic output of other sectors will be indirectly affected due to sectoral linkages, which will lead to changes in pollutant emissions in each sector.\u003c/p\u003e \u003cp\u003eIn order to obtain the net emission abatement potential of each technology, the direct emission reductions during the use stage and the indirectly induced emissions during the production stage are considered in this paper, using 2018 as the base year.\u003c/p\u003e\u003cp\u003eConsidered from the whole life cycle, the equipment and power used in these abatement technologies generate additional pollutant emissions during the production phase. In addition, the reduction in fuel consumption also reduces the pollutants emitted during production stages. The addition emissions in production stage generated through industry linkages, is denote as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(AD{E}_{production}\\)\u003c/span\u003e\u003c/span\u003e. The top-down IOA method can be used to estimate the impact on the whole economic sectors from the final demand shock (reflected in monetary value) caused by the application of the technology, resulting in associated additional emissions in production stage. The SPA model introduced above can calculate the emissions in the production stage, and this method does not need too much micro production process data. We use the SPA model to estimate the additional emissions from the production phase of the technology, as described in Eqs.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)- (\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, where it is important to note that when calculating the additional emissions of a technology, the variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Y\\)\u003c/span\u003e\u003c/span\u003e is set to zero in all sectors except for the shock sector, which is a change in value (denoted as \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e,\u003c/sub\u003e \u003cem\u003ein million of yuan\u003c/em\u003e). The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{1}\\)\u003c/span\u003e\u003c/span\u003e is the fuel cost, which is the final demand reduction in the refined oil sector in the IO table. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{2}\\)\u003c/span\u003e\u003c/span\u003e is the technology investment cost, which is the increase in final demand in the IO table for the automotive parts sector, rubber and related industries such as the complete vehicle manufacturing sector (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) due to the implementation of the motor vehicle retrofit technology. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{3}\\)\u003c/span\u003e\u003c/span\u003e is the electricity cost, which in the IO table is the increased final demand in the electricity sector due to the implementation of motor vehicle electrification technology. Here we set the fuel cost (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{1}\\)\u003c/span\u003e\u003c/span\u003e) and electricity cost (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{3}\\)\u003c/span\u003e\u003c/span\u003e) increase by a discount rate of 5% p.a. in the 15 year\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, and the technology investment cost (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{2}\\)\u003c/span\u003e\u003c/span\u003e) is completed in the first year, without considering discounting. The variation of these three final requirements could be estimated by the following formula.\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$${\\text{y}}_{1}=\\sum _{i=1}^{n}(\\text{O}\\text{P}\\times \\text{F}\\text{E}\\times \\text{A}\\text{M}\\times \\text{V}\\text{O}\\times \\text{S}\\times {10}^{-6}\\times {\\left(1+5\\text{\\%}\\right)}^{n-1})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$${\\text{y}}_{2}=\\text{I}\\text{C}\\times \\text{V}\\text{O}\\times {10}^{-6}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$${\\text{y}}_{3}=\\sum _{i=1}^{n}(\\text{E}\\text{P}\\times \\text{P}\\text{M}\\times \\text{A}\\text{M}\\times \\text{V}\\text{O}\\times {10}^{-6}\\times {(1+5\\text{\\%})}^{n-1})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn which, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(OP\\)\u003c/span\u003e\u003c/span\u003e (yuan/L) is the oil price, and the average oil price in China in 2018 is 7.27 yuan /L for gasoline and 6.88 yuan/L for diesel\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e; the variables \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(FE\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(AM\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(VO\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(S\\)\u003c/span\u003e\u003c/span\u003e are described in Eq.\u0026nbsp;(\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e7\u003c/span\u003e); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(IC\\)\u003c/span\u003e\u003c/span\u003e (yuan) is the technology investment cost (see Table S1); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(EP\\)\u003c/span\u003e\u003c/span\u003e (yuan/kW\u0026middot;h) is the electricity price, and the average feed-in tariff for power producers in China in 2018 is 0.374 yuan/kW\u0026middot;h\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(PM\\)\u003c/span\u003e\u003c/span\u003e (kW\u0026middot;h/100km) is the power consumption per unit mileage (see Table S2); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\\)\u003c/span\u003e\u003c/span\u003e is the year of technology use, ranging from 1 to 15.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eMajor industries where emission reduction technologies are causing\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mathbf{y}}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003echanges\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDirect industries with increased final demand \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEngine energy-saving technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEngine and transmission parts improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAuto parts and accessories industry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNew energy technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdopt new power systems such as electrification and hybrid power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAutomobile industry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDrag reduction technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow friction lubricant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLubricant improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRefined petroleum and processed nuclear fuel products industry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow rolling resistance tire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRubber tire improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRubber products industry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLightweight Or Air resistance reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall body design improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAutomobile industry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor vehicles, the emission reduction caused by energy-saving or substitution technology could be accounted by the coefficient method of pollutant emission per unit of gasoline. Assuming no change in vehicle ownership is considered, the direct CO\u003csub\u003e2\u003c/sub\u003e abatement could be obtained by multiplying the CO\u003csub\u003e2\u003c/sub\u003e generation factor by the amount of fuel that would be saved by each type of technology in the next 15 years. Since NOx emissions are more likely to be influenced by tailpipe control technologies, the relationship between NOx production factor and final emission factor of fuel consumption is highly uncertain. In the absence of sufficient data, this article set the current emissions multiplied by the corresponding energy-saving efficiency of various technologies as the emissions of this technology.\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$${\\text{A}\\text{B}\\text{E}}_{\\text{u}\\text{s}\\text{e}-{\\text{C}\\text{O}}_{2}}\\text{=\u0026alpha; \u0026times;FE\u0026times;VO\u0026times;AM\u0026times;S\u0026times;\u0026rho;\u0026times;1}{\\text{0}}^{\\text{-11}}\\text{\u0026times;15}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ11\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e\n$$\\text{A}\\text{B}{\\text{E}}_{\\text{u}\\text{s}\\text{e}-\\text{N}\\text{O}\\text{x}}\\text{=}\\text{\u0026beta;}\\text{\u0026times;S\u0026times;15}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn which, the CO\u003csub\u003e2\u003c/sub\u003e and NOx direct abatement emissions are denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ABE}_{use-{CO}_{2}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(A{BE}_{use-NOx}\\)\u003c/span\u003e\u003c/span\u003e, respectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e is the CO\u003csub\u003e2\u003c/sub\u003e generation factor, for gasoline is 2.93t/t and for diesel is 3.10t/t; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(FE\\)\u003c/span\u003e\u003c/span\u003e is the fuel consumption per unit mileage(L/100km); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(VO\\)\u003c/span\u003e\u003c/span\u003e is the current total ownership of vehicles; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(AM\\)\u003c/span\u003e\u003c/span\u003e is the average annual mileage (km); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e is the fuel density, 0.73 kg/L for gasoline and 0.84 kg/L for diesel; 15 is the service life of the vehicle; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e is the annual statistical NOx emissions (Kt) of current total quantity of vehicles; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(S\\)\u003c/span\u003e\u003c/span\u003e is the energy-saving efficiency in %. Detailed data for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(FE\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(VO\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(AM\\)\u003c/span\u003e\u003c/span\u003e are shown in Table S2, and detailed data for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(S\\)\u003c/span\u003e\u003c/span\u003eare shown in Table S1.\u003c/p\u003e \u003cp\u003eThe potential for emission abatement over the whole life cycle is referred to as the net abatement potential/emissions, denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ABE}_{net}\\)\u003c/span\u003e\u003c/span\u003e(CO\u003csub\u003e2\u003c/sub\u003e in Mt, NOx in Kt). The net abatement potential is the direct emission reductions from the use phase of the technology deducting the additional emissions generated in the production phase, which could be expressed as:\u003cdiv id=\"Equ12\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ12\" name=\"EquationSource\"\u003e\n$$\\text{A}\\text{B}{\\text{E}}_{\\text{n}\\text{e}\\text{t}}={\\text{A}\\text{B}\\text{E}}_{\\text{u}\\text{s}\\text{e}}-\\text{A}\\text{D}{\\text{E}}_{\\text{p}\\text{r}\\text{o}\\text{d}\\text{u}\\text{c}\\text{t}\\text{i}\\text{o}\\text{n}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e12\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFrom the perspective of the ife cycle, the adoption of vehicle energy-saving or substitution technology will produce additional investment costs of technological transformation (above \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{1}\\)\u003c/span\u003e\u003c/span\u003e). If electric vehicle technology is adopted, it will increase additional power costs (above \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{2}\\)\u003c/span\u003e\u003c/span\u003e), but it will also save fuel and generate energy-saving benefits (above \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{3}\\)\u003c/span\u003e\u003c/span\u003e). Economic benefits are the main driving force that may promote the long-term development of technology. We generally refer to emission reduction from the perspective of cost, however, but energy-saving technology may bring economic benefits. From the perspective of cost-benefit analysis, the net benefit (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(NB\\)\u003c/span\u003e\u003c/span\u003e) is as follows.\u003cdiv id=\"Equ13\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ13\" name=\"EquationSource\"\u003e\n$$\\text{N}\\text{B}={\\text{y}}_{3}-{\\text{y}}_{1}-{\\text{y}}_{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e13\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe net abatement benefit per unit emissions (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(AB\\)\u003c/span\u003e\u003c/span\u003e, CO\u003csub\u003e2\u003c/sub\u003e unit yuan/t, NOx unit yuan/kg) could be calculated as follows:\u003cdiv id=\"Equ14\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ14\" name=\"EquationSource\"\u003e\n$$\\text{A}\\text{B}=\\text{N}\\text{B}/{\\text{A}\\text{B}\\text{E}}_{\\text{n}\\text{e}\\text{t}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e14\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe historical base period of this study is from 2012 to 2018. For the future emission reduction potential, this study takes 2018 as the base year and set the future research period as 15 ye\u003c/p\u003e\n\u003cp\u003eThe historical base period of this study is from 2012 to 2018. For the future emission reduction potential, this study takes 2018 as the base year and set the future research period as 15 years after 2018 (determined based on the current end-of-life requirements for most vehicle models in China[\u003csup\u003e32\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e). We organize the economic sectors in the original input-output tables for 2012 and 2018 into 142 sectors (139 sectors in the original table for 2012 and 153 sectors in the original table for 2018 of China) according to the latest industrial classification and coding requirements of the national economy[\u003csup\u003e33\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.The transportation sector included 11 subsectors: rail passenger transport, rail freight transport, road passenger transport, road freight transport, water passenger transport, water freight transport, air passenger transport, air freight transport, pipeline transport, multimodal transport, and postal service.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ers after 2018 (determined based on the current end-of-life requirements for most vehicle models in China[\u003csup\u003e32\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e). We organize the economic sectors in the original input-output tables for 2012 and 2018 into 142 sectors (139 sectors in the original table for 2012 and 153 sectors in the original table for 2018 of China) according to the latest industrial classification and coding requirements of the national economy[\u003csup\u003e33\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.The transportation sector included 11 subsectors: rail passenger transport, rail freight transport, road passenger transport, road freight transport, water passenger transport, water freight transport, air passenger transport, air freight transport, pipeline transport, multimodal transport, and postal service.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince the transportation sector is not the main emission sector of sulfur dioxide and particulate matter, and considering the availability of data, NOx is only regarded as the representative of air pollutants in this paper. The NOx emissions data for each sector are from the National Bureau of Statistics of China, and NOx emissions of motor vehicles are provided by the Annual Report on Environmental Management of Mobile Sources in China (2019)\u0026nbsp;[\u003csup\u003e34\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e and converted to NOx emission reductions based on energy savings for each type of technology; CO\u003csub\u003e2\u003c/sub\u003e data for each sector of China are from CEADs (https://www.ceads.net.cn/data/), and CO\u003csub\u003e2\u003c/sub\u003e emission reduction data involving specific emission reduction technologies for motor vehicles are based on energy-related emission factors provided by the IPCC[\u003csup\u003e35\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, and CO\u003csub\u003e2\u003c/sub\u003e emission reductions are obtained based on the energy savings of each type of technology.\u003c/p\u003e\n\u003cp\u003eFor the future development of automotive emission reduction technology, the \"New Energy Vehicle Industry Development Plan (2021-2035)\" in China clearly states that pure electric vehicles, plug-in hybrid vehicles, fuel cell vehicle technology and common energy-saving technologies such as light weight and low frictional resistance are the future development direction. However, China lacks systematic and open literature on these technologies. Here, referring to the reports of the National Science Research Council and the National Highway Traffic Safety Administration in the U.S.A[\u003csup\u003e36\u003c/sup\u003e\u003csup\u003e][\u003c/sup\u003e\u003csup\u003e37\u003c/sup\u003e\u003csup\u003e][\u003c/sup\u003e\u003csup\u003e38\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e and the research by Peng et al.[\u003csup\u003e11\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e,\u0026nbsp;this paper classifies emission reduction technologies into 3 categories: engine energy-saving technology, new energy technology, and drag reduction technology. Besides, 3 types of passenger cars, light trucks and heavy trucks are considered. Since upgrading exhaust gas control measures at this stage may lead to a slight increase in fuel consumption and CO\u003csub\u003e2\u003c/sub\u003e emissions, such measures are not involved in this paper. The names, energy efficiency and investment cost of each technical scenario and parameters of each type of automotive are shown in Table S1 and Table S2, respectively.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang, R., Fujimori, S., \u0026amp; Hanaoka, T. The contribution of transport policies to the mitigation potential and cost of 2 C and 1.5 C goals. Environ. Res. Lett.13(5), 054008(2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIEA, 2019a. World Energy Balance 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChina; Second National Pollution Source Census Bulletin; Ministry of Ecology and Environment of the People's Republic of China, National Bureau of Statistics, Ministry of Agriculture and Rural Affairs of the People's Republic of China,2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu X., Wu Y., \u0026amp; Zhang S., et al. 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Pasedena, CA: California Hybrid, Efficient and Advanced Truck Research Center.(2013).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1508775/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1508775/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe transportation sector has a significant impact on the synergistic reduction of CO\u003csub\u003e2\u003c/sub\u003e and pollutants. Using a structural path decomposition, we reveals the direct and embodied CO\u003csub\u003e2\u003c/sub\u003e and NOx emissions from the transportation sector triggered by key supply chain pathways. Meanwhile, the emission abatement potential and economic costs of 33 vehicle specific abatement technology options are analyzed based on the input-output analysis for life cycle assessment. The results show that most of the vehicle technology options can reduce CO\u003csub\u003e2\u003c/sub\u003e and NOx emissions while saving economic costs. Among these technologies, both the pure electric technology for passenger cars and the parallel hybrid technology for heavy-duty trucks have high abatement potential. Compared to costly pure electrification technologies for passenger cars, parallel hybrid technologies for heavy-duty trucks offer the highest economic benefits as well as a better driving mileage. Therefore, hybrid heavy-duty trucks will be a more comprehensive solution in the near future.\u003c/p\u003e","manuscriptTitle":"Finding the best approach for the transportation sector to synergistically reduce CO2 and air pollutants emissions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-04-26 19:23:13","doi":"10.21203/rs.3.rs-1508775/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"291276cb-ba88-4f4a-8c23-7fb126a5b848","owner":[],"postedDate":"April 26th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-05-26T09:05:37+00:00","versionOfRecord":[],"versionCreatedAt":"2022-04-26 19:23:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1508775","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1508775","identity":"rs-1508775","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}